DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed on April 28, 2026 has been entered. Claims 1–8 are currently pending. Claims 1 and 4–6 have been amended.
Response to Arguments
Applicant’s arguments filed 04/28/2026 have been fully considered. Some arguments are persuasive with respect to the rejections under 35 U.S.C. § 112(a), § 112(b), and § 103, while others regarding the 35 U.S.C. § 101 rejection is not persuasive, as explained below.
Applicant's arguments regarding the previous written description rejection of claims 2–4 under 35 U.S.C. § 112(a) are persuasive. The Applicant has amended claim 4 to remove the word "solely". Applicant’s arguments, see pages 5–7 of the Remarks, point to paragraphs and of the specification, which describe storing assignments between layer thickness variations and grayscale value variations in data sets, linking multiple data sets in a data packet, and using that packet to train an artificial intelligence that creates layer-thickness-variation profiles for further substrates from grayscale images. Therefore, the prior written-description rejection under 35 U.S.C. § 112(a) for claims 2–4 has been withdrawn. It is noted the amendments do raise a new § 112(a) rejection, as set forth in the Rejections section.
Applicant's arguments regarding the previous indefiniteness rejection of claims 5 and 6 under 35 U.S.C. § 112(b) are also persuasive. Claim 5 has been amended to clarify the relative locations of the excluded regions, and claim 6 has been amended to recite "highest grayscale differences", which is explicitly defined in paragraph of the specification. Therefore, the prior written-description rejection under 35 U.S.C. § 112(b) for claims 5–6 has been withdrawn.
Applicant's arguments filed 04/28/2026 have been fully considered but are not persuasive with respect to the rejection under 35 U.S.C. § 101. Applicant argues that amended claim 1 is directed to a practical application because it now recites performing further processing of regions of the substrate based on the created layer-thickness variation profile. The Examiner respectfully disagrees. The added “further processing” limitation is recited only at a high level and does not specify a particular substrate-processing step, tool, process parameter, control rule, or technological improvement. The claim remains directed to collecting grayscale/ thickness data, calculating numerical variations, associating numerical profiles, and using the result in a generic post-analysis step. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
Applicant's arguments regarding the previous rejection of claim 1 under 35 U.S.C. § 103 (over Iwanaga alone) have been fully considered. Applicant argues that Iwanaga only calculates absolute, specific layer thicknesses and does not "create a layer thickness variation profile". However, the Examiner notes that the rejection has been updated to a new ground of rejection relying on the combined teachings of Iwanaga, Bartov, and Dhandapani. This modification to the rejection is necessitated by Applicant's amendment adding the "variation profile" and "further processing" limitations to independent claim 1.
Applicant’s “teaching away” argument is not persuasive because the references do not criticize, discredit, or discourage using region-based groupings or AI-driven profiling of thickness variations. Iwanaga teaches acquiring wafer images, correlating pixel/ grayscale values with measured film thickness values, and calculating thickness per pixel across a wafer. In view of these teachings, defining regions that group multiple measurement fields, computing characteristic values and variation profiles for those regions, excluding regions that fall outside desired ranges (e.g., edge/center regions or regions with high roughness), and performing further processing only on remaining regions is a highly obvious extension of the prior art and produces predictable results. A reference does not teach away merely by disclosing a different embodiment than the one claimed. See In re Fulton, 391 F.3d 1195, 1201 (Fed. Cir. 2004).
Applicant's arguments attack the references individually and do not address the combined teachings as applied in the updated rejection. The newly added limitations have been considered and addressed in the updated rejection using Iwanaga, Bartov, and Dhandapani. Because the need to apply Bartov and Dhandapani to independent claim 1 was directly necessitated by Applicant's amendment adding the new variation-profile and further-processing limitations, this action is properly made final in accordance with MPEP § 706.07(a).
Applicant's arguments regarding claims 5 and 6 are not persuasive. Applicant argues that Chen does not cure the alleged deficiencies of Iwanaga because Chen is directed to defect inspection and does not convert image information into layer thickness information. The Examiner respectfully disagrees. The new base combination of Iwanaga, Bartov, and Dhandapani already teaches the correlation of image information to layer thickness information. Chen is cited specifically for teaching the limitation of excluding regions on a substrate edge or center. Chen teaches utilizing edge exclusion parameters and spatial masks to purposely omit edge regions from measurement to avoid data discontinuity and edge-response anomalies, applying Chen's threshold identification to Bartov's grayscale variation metrics perfectly maps to selecting regions having the "highest grayscale differences".
Applicant's arguments regarding claim 7 are not persuasive. Applicant argues that Uziel deals exclusively with scanning and does not perform layer thickness determinations. Again, the base combination of Iwanaga, Bartov, and Dhandapani teaches the thickness determinations. Uziel is cited solely for the teaching of selecting measurement fields that partially overlap. One of ordinary skill would incorporate overlapping fields into the base combination to ensure complete coverage and prevent data gaps during scanning.
Based on these facts, this action is made FINAL.
Claim Objections
Claim 4 is objected to because of the following informalities: Claim 4 recites “the layer respective thickness variation profile”, it should be read “the respective layer thickness variation profile”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1–8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 has been amended to recite “creating a grayscale value variation profile for each of said measuring points”, “creating a layer thickness variation profile for each of said measuring points”, and “associating the layer thickness variation profile of each of said measuring points with the corresponding grayscale value variation profile of each of said measuring points”. However, the originally filed specification describes determining grayscale value variations, determining layer thickness variations, associating the variations, and creating a layer thickness variation profile of the surface layer generally, but it does not clearly describe possession of a distinct profile for each measuring point as now claimed. The disclosure describes inter-point variation determination and generation of a layer thickness variation profile for the measured region or overall substrate area, not a separate grayscale value variation profile and a separate layer thickness variation profile for each individual measuring point. The written description therefore does not reasonably convey possession of the amended per-measuring-point profile limitations.
Claim 1 has also been amended to recite “performing further processing of regions of the substrate based on the layer thickness variation profile created”. Although the specification describes that a layer thickness variation profile may be used to determine whether regions are suitable for further processing [0005] & [0027], the specification does not describe actually performing further processing of regions of the substrate based on the created profile as a positively recited step of the claimed method. The disclosure supports evaluation of suitability for further processing, but not possession of the presently claimed affirmative processing step itself. Accordingly, the written description does not reasonably convey possession of the full scope of “performing further processing of regions of the substrate based on the layer thickness variation profile created”.
Claim 4 recites linking a plurality of data sets in a data packet and using the data packet to train an artificial intelligence model, wherein the artificial intelligence model creates the layer respective thickness variation profile from grayscale values or grayscale value variations of grayscale images captured from further substrates. The specification states, in a results-oriented manner, that a “data packet is used to train an artificial intelligence” and that the artificial intelligence creates thickness variation profiles from grayscale values or grayscale value variations. However, the specification does not disclose the algorithmic details for the claimed AI/ data-packet functionality, including the input representation, training target, model architecture, training procedure, validation criteria, or how the linked data sets are structured and used to train the model to create the claimed thickness variation profile for further substrates. The written description therefore does not reasonably convey possession of the full scope of claim 4.
Because claims 2–8 depend from claim 1, they inherit the written-description deficiencies of claim 1 and fail to cure the deficiencies. Claim 4 is further deficient for the additional artificial-intelligence/data-packet reasons discussed above. Therefore, claims 1–8 are rejected under 35 U.S.C. 112(a) for lack of written description.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “creating a grayscale value variation profile for each of said measuring points” and “creating a layer thickness variation profile for each of said measuring points”. However, the claim also recites determining grayscale value variations and layer thickness variations between measuring points. The claim is unclear as to how a variation determined between measuring points results in a “variation profile for each” individual measuring point. The claim does not define whether the claimed profile is (i) a single value assigned to each measuring point, a local profile around each measuring point, (ii) a profile over multiple measuring points, or (iii) a global surface profile indexed by measuring point. Because one of ordinary skill in the art cannot determine with reasonable certainty what constitutes the claimed “variation profile for each of said measuring points”. It is unclear what constitutes a “profile” for a single measuring point. A profile ordinarily implies a set, curve, distribution, or variation across multiple positions or values, whereas the claim ties the profile to each individual measuring point. For the purpose of examination, the Examiner interpret this limitation as comparing these recorded values against a baseline, then plots these changes into a "variation profile" which is essentially a distribution graph or data set mapping out the condition of the points.
Because claims 2–8 depend from claim 1, they inherit these ambiguities and fail to cure the deficiencies. Accordingly, claims 2–8 are rejected under 35 U.S.C. 112(b) for at least the same reasons as claim 1.
Under Nautilus, Inc. v. Biosig Instruments, Inc., 572 U.S. 898 (2014), a claim is indefinite if it fails to inform those skilled in the art about the scope of the invention with reasonable certainty. Because the claim language identified above fails to provide objective boundaries and produces multiple reasonable interpretations, claims 1–8 are indefinite under 35 U.S.C. 112(b).
Claims 1–8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are:
In amended Claim 1, the applicant added the final step of “performing further processing of regions of the substrate based on the layer thickness variation profile created”. The term “further processing” is unclear as it does not define what acts are actually occurring. A person having ordinary skill in the art cannot determine whether "further processing" means running another computer algorithm on the data (computational processing) or physically altering the wafer, such as etching, cutting, or polishing it (physical processing). Because there are no boundaries or objective standards provided in the claim or the specification to define what constitutes "further processing", the scope of the claim is unclear.
Because one of ordinary skill in the art cannot determine with reasonable certainty what physical or computational actions constitute infringement of this step, the claim term "further processing" renders claim 1 incomplete. Because claims 2–8 depend from claim 1, they inherit this ambiguity and fail to cure the deficiency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–8 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without significantly more.
This rejection has been made in accordance with the current USPTO subject matter eligibility framework, including MPEP §§ 2103–2106.07, the 2019 Revised Patent Subject Matter Eligibility Guidance, the October 2019 Patent Eligibility Guidance Update, the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the July 2024 AI Subject Matter Eligibility Examples, the August 4, 2025 USPTO memorandum titled “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. § 101,” and the USPTO’s guidance concerning Ex parte Desjardins, Appeal No. 2024-000567. The claims have been evaluated under the broadest reasonable interpretation, and the claims have been considered as a whole.
Step 1: Statutory category
Independent claim 1 is directed to “a method for creating a layer thickness variation profile of a surface layer of a substrate” and therefore falls within the statutory category of a process. Claims 2–8 depend, directly or indirectly, from claim 1 and are likewise directed to process subject matter. Accordingly, the analysis proceeds to Step 2A.
Step 2A, Prong One (Judicial exception)
Independent claim 1 recites, in summary: capturing a grayscale image of a substrate surface with a camera; selecting measurement fields on the substrate surface from regions of the grayscale image; measuring layer thicknesses at a plurality of measuring points with a measuring device; determining grayscale value variations between grayscale values present at measuring points; determining layer thickness variations by calculating differences between layer thicknesses measured at the measuring points; associating the layer thickness variations determined between the measuring points with corresponding grayscale value variations determined between the respective measuring points; creating the layer thickness variation profile of the surface layer based on the grayscale values of the grayscale image; and performing further processing of regions of the substrate based on the layer thickness variation profile created.
These limitations recite an abstract idea, namely collecting information, analyzing the information, correlating two sets of information, and using the result to guide a decision or process. The claim requires: (1) gathering image information (grayscale values) and physical measurement information (layer thickness values); (2) performing mathematical comparisons to determine “variations” by calculating differences between values; (3) associating or mapping those variations to each other; and (4) using that correlation to create a profile and decide how substrate regions should be processed.
The “grayscale values” and “layer thicknesses” are treated as data inputs to an analysis process. The “grayscale value variations”, “layer thickness variations”, “profiles” and “data set” are treated as mathematical quantities and information items in a data-correlation and evaluation process. The claim does not recite an improvement to how the camera captures or encodes images, how the measuring device physically measures thickness, or how a computer stores or transmits data. Rather, the claim uses conventional imaging and metrology components to obtain data and then performs mathematical and logical operations on that data.
The claim is similar in character to claims that courts have found abstract where the focus is on collecting information, analyzing the information, and presenting or acting on the results. In Electric Power Group, the Federal Circuit recognized claims directed to gathering and analyzing information of a specified content, then displaying the results, as abstract. Here, the claimed method gathers grayscale and thickness information, mathematically compares values to determine “variations”, correlates those variations, and uses the result to direct further processing.
The claims are also consistent with the reasoning of AI Visualize, Inc. v. Nuance Communications, Inc., where the Federal Circuit looked to the character of the claims as a whole and affirmed ineligibility where the claims were directed to obtaining, manipulating, and using information at a high level of generality rather than to a specific improvement in computer functionality. Here, the character of claim 1 as a whole is image/ thickness data acquisition, mathematical variation-profile generation, numerical association/ mapping, and generic use of the result, not an improvement to camera technology, optical metrology hardware, substrate-processing equipment, or computer technology itself.
Dependent claims 2–8 recite additional limitations that likewise fall within the abstract idea:
Claim 2 recites storing the layer thickness variations and grayscale value variations in a data set, capturing at least one further grayscale image of a further substrate surface, and utilizing the data set to create a layer thickness variation profile of a surface layer of the further substrate on the basis of grayscale values of the further grayscale image. This recites collecting and storing information and reusing the same correlation for another instance, i.e., further data collection and analysis.
Claim 3 recites assigning the layer thickness variations contained in the data set to different layer thickness variation ranges and assigning the ranges to at least one of the grayscale value variations. This recites data categorization and mapping of numerical values into intervals, which is a form of abstract data analysis.
Claim 4 recites storing multiple data sets in a data packet and using the data packet to train an artificial intelligence model that creates a layer thickness variation profile from grayscale values or grayscale value variations. This recites using a generic AI/ machine-learning model as a mathematical tool to perform the same underlying prediction or correlation task and does not recite a specific improvement to AI or computer technology itself.
Claim 5 recites excluding regions on an edge or center of the substrate from selection. This recites a rule for selecting which information to include or exclude from the analysis.
Claim 6 recites selecting measurement fields in regions having the highest grayscale differences. This recites an information-selection heuristic based on observed data values.
Claim 7 recites selecting measurement fields that partially overlap. This recites a sampling scheme for information collection; it adjusts where data is gathered but does not change the abstract nature of the underlying correlation and evaluation process.
Claim 8 recites that the surface layer is transparent and the measuring device is optical. This limits the environment of use but does not change the fundamental character of the claim as an information-collection-and-analysis method.
Accordingly, claims 1–8 recite an abstract idea under Step 2A, Prong One.
Step 2A, Prong Two (Practical application)
The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application.
The additional elements include capturing a grayscale image with a camera, selecting measurement fields, measuring layer thicknesses with a measuring device at measuring points, and performing “further processing of regions of the substrate based on the layer thickness variation profile created”. These elements do not integrate the mathematical concept into a practical application. The camera and measuring device merely gather input data for the mathematical analysis. The selecting of measurement fields merely identifies where data is collected. These are data-gathering activities used to feed the claimed mathematical processing.
The amended limitation “performing further processing of regions of the substrate based on the layer thickness variation profile created” also does not integrate the exception into a practical application because it is recited at a high level of generality. The claim does not identify what specific processing is performed, what substrate-processing tool is controlled, what process parameter is adjusted, what physical manufacturing operation is carried out, what technical rule selects the regions, or how the layer thickness variation profile technically controls a physical process. Thus, the amended limitation merely recites a generic post-analysis use of the calculated profile rather than a particular technological application or improvement.
The claim does not recite a particular improvement to camera technology, grayscale-image processing technology, thickness-measurement technology, substrate-processing technology, or computer technology. The claim does not improve how a camera captures a grayscale image, how a measuring device measures thickness, how the substrate is physically processed, or how a computer performs calculations. Instead, the claim applies conventional data acquisition and mathematical correlation in the field of substrate/layer-thickness analysis.
Claim 4’s artificial-intelligence limitation does not change the eligibility analysis. Claim 4 recites linking data sets in a data packet and using the data packet to train an artificial intelligence model that creates the layer thickness variation profile from grayscale values or grayscale value variations. However, claim 4 does not recite a particular AI model architecture, parameter-update mechanism, training rule, loss function, memory-saving arrangement, inference-speed improvement, or other technical improvement to machine-learning technology itself. The AI model is recited functionally as a mathematical tool for generating the same profile from grayscale data.
This analysis is consistent with the USPTO’s 2024 AI subject matter eligibility guidance and AI examples, which emphasize that AI-related claims may be eligible when they recite a specific technological improvement or otherwise integrate a judicial exception into a practical application. The present claims do NOT recite such a specific technological improvement.
This case is distinguishable from Ex parte Desjardins. In Desjardins, the claims were treated as reflecting an improvement in machine-learning technology itself, including training a model on a series of tasks while preserving prior knowledge and reducing complexity/ storage burdens. Here, claim 4 does NOT recite a particular training technique that improves the operation of the AI model, reduces model storage, preserves prior knowledge, improves inference speed, or otherwise improves machine-learning technology. Rather, the AI model is used as a generic tool to create a layer thickness variation profile from grayscale values or grayscale value variations.
Nor does limiting the abstract idea to the environment of substrate inspection or layer-thickness metrology make the claims eligible. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit rejected eligibility where claims applied machine-learning techniques to a field of use without claiming an improvement to machine-learning technology itself. Similarly here, applying mathematical variation-profile generation and generic AI prediction to substrate/ layer-thickness analysis is a field-of-use limitation, not an integration of the judicial exception into a practical application.
Accordingly, the claims do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
Step 2B: (Inventive concept)
The additional elements, considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The camera, measuring device, data set, and AI model are conventional tools used in their ordinary manner to capture, measure, store, and process data. The specification describes using known cameras and known thickness measuring devices (interferometers, reflectometers, etc.) to obtain data which is then analyzed according to the claimed correlation logic. These elements are recited at a high level of generality and perform their routine functions.
The ordered combination also does not provide an inventive concept. The ordered combination follows the abstract idea itself: acquire grayscale and thickness data, calculate variations/ differences, create variation profiles, associate grayscale variation profiles with thickness variation profiles, generate a thickness variation profile from grayscale values, and generically perform further processing based on the profile. This is no more than the abstract mathematical relationship implemented using conventional data-gathering and data-processing steps.
Dependent claims 2–8 recite additional steps of storing data, categorizing variations into ranges, using AI to model the correlation, excluding certain regions, preferring certain regions, overlapping measurement fields, and using an optical measuring device for a transparent layer. These limitations amount to routine data-handling, data-selection, and environment-of-use restrictions that are well-known in metrology and manufacturing quality control and do not provide an inventive concept beyond the abstract idea.
Accordingly, claims 1–8 are directed to a judicial exception without significantly more and are therefore rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga (Iwanaga et al, US 2015/0324970 A1, 2015), in view of Bartov (Bartov et al, US 2005/0179910 A1, 2005), further in view of Dhandapani (Dhandapani et al, US 2021/0407065 A1, 2021).
Regarding claim 1, Iwanaga teaches a method for creating a layer thickness variation profile of a surface layer of a substrate, the method comprising the following steps:
capturing a grayscale image of a substrate surface of the substrate with a camera;
( Iwanaga teaches an imaging unit that images a surface of the substrate [0012], and the imaged image is RGB or a gray scale, [0059], [0086]. )
selecting a plurality of measurement fields on the substrate surface from regions of the grayscale image;
( Iwanaga teaches extracting pixel values from the imaged image at pixels corresponding to a plurality of coordinates on the wafer surface (i.e., selecting, from regions of the imaged image, plural regions or pixels that correspond to plural locations on the substrate surface) [0069]; and further teaches extracting pixel values for a plurality of pixels in the imaged image (e.g., hundreds of points) and that the number of extracted pixels “can be arbitrarily set,” i.e., selecting a plurality of image regions/pixels as measurement fields [0072], [0086]. )
measuring layer thicknesses of the surface layer of the substrate with a measuring device at a plurality of measuring points in each of the measurement fields;
( Iwanaga teaches measuring the film thickness of a film formed on the upper surface of a measurement preparation wafer using a film thickness measurement means, where a thicknessmeter utilizing reflectance spectroscopy is used [0065]; and further teaches that the measurement is performed at a plurality of points (e.g., 51 points) on the wafer and that the film thickness measured values are acquired for each measurement point together with corresponding coordinates in a measured value table [0066]–[0067], and similarly teaches that “the film thicknesses at a plurality of points are measured” and acquired for each measurement point [0119]. )
determining grayscale value variations between the grayscale values present at measuring points of the measurement fields of the grayscale image and creating a grayscale value variation distribution of said measuring points;
( [0069], [0071–0072], [0074], [0086]: Iwanaga teaches extracting, for each coordinate corresponding to each measuring point, the pixel value from the imaged image and generating a pixel value extraction table. Iwanaga further teaches that a grayscale may be used for the imaged image, and teaches selecting image data having the largest change amount in pixel value relative to the change in film thickness measured value. Iwanaga also teaches generating a pixel value data table from extracted pixel values of pixels at a plurality of points, wherein increasing the number of extracted pixel values improves the accuracy of the film thickness distribution within the wafer. Thereby determining grayscale/ pixel-value variations between measuring points and creating a grayscale/ pixel-value variation distribution of said measuring points. )
determining layer thickness variations by calculating a difference between the layer thicknesses measured at the measuring points of the measurement fields and creating a layer thickness variation distribution of said measuring points;
( [0014], [0065–0067], [0071–0074]: Iwanaga teaches acquiring film thickness measured values at a plurality of points and coordinates corresponding to the film thickness measured values for a film having a non-uniform thickness, and storing the acquired measured value table for each measurement point. Iwanaga further teaches evaluating change in film thickness measured value in the correlation processing and generating a film thickness distribution chart by plotting calculated film thicknesses at known coordinates on the wafer. Thus, Iwanaga teaches determining layer/ film-thickness variations between measuring points and creating a layer/ film-thickness variation distribution of said measuring points. )
associating the layer thickness variations determined between the measuring points with corresponding grayscale value variations determined between the respective measuring points by associating the layer thickness variation distribution of said measuring points with the corresponding grayscale value variation distribution of said measuring points;
( [0014], [0069–0074], [0086]: Iwanaga teaches acquiring film thickness measured values at a plurality of points and coordinates corresponding to the film thickness measured values for a film having a non-uniform thickness, and storing the acquired measured value table for each measurement point. Iwanaga further teaches evaluating change in film thickness measured value in the correlation processing and generating a film thickness distribution chart by plotting calculated film thicknesses at known coordinates on the wafer, thereby determining layer/ film-thickness variations between measuring points and creating a layer/ film-thickness variation distribution of said measuring points. )
creating the layer thickness variation profile of the surface layer based on the grayscale values of the grayscale image; and
( Iwanaga teaches that a grayscale is used for the imaged image [0086], and teaches calculating film thickness on the basis of a pixel value of the imaged image and the correlation data [0014], and generating a film thickness distribution chart/ profile over the substrate; [0074], [0080]. )
Although Iwanaga provides the framework for extracting pixel values at measurement points and generating correlation data to calculate film thickness for each pixel, Bartov further teaches that differences in gray-level or intensity between corresponding regions of dies are directly indicative of thin-film thickness variations:
determining grayscale value variations between the grayscale values of corresponding regions of the grayscale image and creating a grayscale value variation profile for each of said measuring points;
( [0051–0054], [0065–0074], [0078–0083]: Bartov teaches scanning a wafer to obtain a wafer image including die images composed of pixels, identifying a first region in a first die image and obtaining a first intensity/ gray-level measurement, identifying a corresponding region in a second die image and obtaining a second intensity/ gray-level measurement, wherein the corresponding region has the same location/ offset as the first region. Bartov further teaches that thickness variations are mapped to detected signal gray-level (GL) variations, and teaches calculating signal/ color variation for each region by subtracting the first GL region image from the second GL region image, accumulating the difference-region image value, and calculating average and standard deviation values. Therefore, Bartov teaches determining grayscale/ GL value variations between corresponding regions and creating a grayscale /GL value variation profile, the claimed measuring points correspond to pixels in the corresponding regions. )
determining layer thickness variations based on the grayscale value variations being indicative of thickness variations between the corresponding regions and creating a layer thickness variation profile for each of said measuring points;
( [0051–0055], [0073–0075]: Bartov teaches that thickness variations are mapped to detected signal gray-level variations between ideally identical pixels, and that GL variations arising from thickness variations across the wafer are distinguishable from abrupt pixel changes caused by defects. Bartov further teaches that, when thickness variation is encountered, the gray-level difference accumulated in the difference-region image reflects the thickness variation, and that the calculated average value indicates the thickness variation. Bartov also teaches processing first and second sets of pixels/ signal intensities to obtain detected signal variations that are indicative of thickness variation between corresponding regions, determining layer-thickness variations based on grayscale/ GL value variations being indicative of thickness variations and creating thickness-variation information/ profile for the corresponding pixels/ regions. )
associating the layer thickness variations determined between the corresponding regions with corresponding grayscale value variations determined between the respective corresponding regions by associating the layer thickness variation profile of each of said measuring points with the corresponding grayscale value variation profile of each of said measuring points;
( [0054], [0059–0061], [0074–0075], [0097–0108]: Bartov teaches that the detected signal/ gray-level variation measurement is indicative of thickness variation between corresponding regions. Bartov further teaches measuring how gray levels move on average between dies, subtracting the measured value from the difference/ error image, and filtering out the contribution of detected signal variation due to thickness variation. Bartov also teaches that the relevant average value for corresponding regions is indicative of thickness variation and that such thickness-variation-related signal data may be fed to a defect-detection subsystem, thereby associating thickness variations between corresponding regions with the corresponding grayscale/ GL value variations for those same regions, i.e., associating a thickness-variation profile with a corresponding grayscale-value variation profile based on the corresponding pixels of the regions. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Bartov’s gray-level/ signal-variation processing into Iwanaga because both references address optical image-based evaluation of thin-film thickness on substrates, and Bartov expressly recognizes that thickness variations are reflected as gray-level signal variations. The modification would have been the predictable use of a known thin-film variation-measurement technique to improve Iwanaga’s coordinate-based pixel-value/ thickness-correlation method by generating variation information corresponding to non-uniform film thickness across the wafer.
Iwanaga [as modified by Bartov] teaches generating thickness-variation information from grayscale/ pixel-value information and associating such grayscale/ gray-level variations with corresponding thickness variations. However, Iwanaga [as modified by Bartov] does not expressly teach the amended requirement of performing further processing of regions of the substrate based on the created layer thickness variation profile, where Dhandapani teaches:
performing further processing of regions of the substrate based on the layer thickness variation profile created.
( [0060], [0074–0077]: Dhandapani teaches that one or more regions on the substrate are selected and that an image is generated for each selected region; the image-processing algorithm outputs a layer-thickness measurement for the particular region. Dhandapani further teaches feeding the generated thickness measurement to a process-control module, where the process-control module adjusts, based on thickness measurements of one or more regions, process parameters such as carrier-head pressure and platen rotation rate, and the adjustment is performed for a polishing process on the substrate or a subsequent substrate. Accordingly, Dhandapani teaches using region-based layer-thickness information to control/ adjust subsequent CMP processing of substrate regions, which corresponds to performing further processing of regions of the substrate based on the created layer-thickness variation profile. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Dhandapani’s region-based CMP process-control technique into Iwanaga [as modified by Bartov], because each reference concerns image-based determination of thin-film thickness information on substrates, and Dhandapani expressly teaches using region-based thickness measurements to adjust polishing process parameters. The modification would have been the predictable use of known image-derived thickness information to control further substrate processing, thereby improving process uniformity and enabling corrective polishing based on the created thickness-variation profile.
Regarding claim 2, Iwanaga [as modified by Bartov and Dhandapani] teaches the method according to claim 1, which comprises:
storing in a data set the layer thickness variations determined between the measuring points together with the grayscale value variations determined between the corresponding measuring points;
( Iwanaga teaches storage part 160 storing or maintaining film thickness measured values with coordinates and extracting, storing pixel values corresponding to the coordinates in tables used to generate correlation data (pixel value extraction table/ correlation data table) [0062]. )
capturing at least one further grayscale image of a further substrate surface of a further substrate; and
( Iwanaga teaches imaging a subsequent wafer W (film thickness measurement object) to capture further an imaged image [0114]. )
utilizing the data set to create the layer thickness variation profile of a surface layer of the further substrate on a basis of grayscale values of the further grayscale image of the further substrate surface of the further substrate.
( Iwanaga teaches calculating film thickness of the resist film formed on the wafer W using the imaged image and the correlation data, thereby obtaining the film thickness distribution over the wafer W; [0072-0074], [0115-0116]. )
Regarding claim 3, Iwanaga [as modified by Bartov and Dhandapani] teaches the method according to claim 2, which comprises assigning the layer thickness variations contained in the data set to different layer thickness variation ranges, and assigning the individual layer thickness variation ranges to at least one of the grayscale value variations, respectively.
( Iwanaga teaches generating correlation data and a correlation graph/ approximation between film thickness measured values and pixel values [0070–0071], and using the correlation (e.g., an approximation expression including linear, quadratic or higher expression) to convert pixel values (including different pixel variations) into corresponding film thickness values (including different film thickness variation ranges) across the wafer to produce the film thickness distribution chart [0073–0074]. Applying the correlation function across the entire pixel domain necessarily establishes an assignment where every grayscale interval (range of pixel values) corresponds to a thickness interval (thickness variation ranges). )
Regarding claim 4, Iwanaga [as modified by Bartov and Dhandapani] teaches the method according to claim 2, which comprises storing a plurality of the data sets which have been determined for different substrates and linking the plurality of data sets in a data packet, and using the data packet to train an artificial intelligence model which creates the layer respective thickness variation profile from the grayscale values or the grayscale value variations of grayscale images captured from further substrates.
( Iwanaga, [0067–0074], teaches creating and using image-value/ thickness correlation data that associates pixel values, including grayscale/ pixel intensity values, with film-thickness measured values at corresponding coordinates for a preparation wafer, and then using the correlation data to calculate film-thickness profile information for other wafers. Dhandapani, [0048–0052], teaches storing a plurality of data sets as records in a database, each record including a calibration image having pixel intensity values and a corresponding ground-truth thickness measurement for a substrate region/ die, and using the combined data set as training data for a neural network/ supervised deep-learning algorithm, i.e., an artificial intelligence model. Dhandapani further teaches applying the trained model to images from new/ further substrates so that, based on input intensity values of pixels of the images, the model outputs layer-thickness measurements and thereby thickness-profile information for the further substrates, [0048–0052]. )
Regarding claim 8, Iwanaga [as modified by Bartov and Dhandapani] teaches the method according to claim 1, wherein the surface layer is transparent, and the measuring device is an optical measuring device.
( Iwanaga teaches the surface layer is a resist oxide film (transparent and can be measured optically via reflected light) formed on a wafer [0087] & [0103–0105], and teaches film thickness measurement means using a thickness meter utilizing reflectance spectroscopy [0102] (optical measuring device). In optical lithography, a photoresist film is required to be transparent for pattern transfer through the film thickness. )
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga [as modified by Bartov and Dhandapani], in view of Chen (Chen et al, US 2013/0236085 A1, 2013).
Regarding claim 5, Iwanaga [as modified by Bartov and Dhandapani] teaches the method according to claim 1, which comprises,
Iwanaga [as modified by Bartov and Dhandapani] teaches selecting and processing substrate regions/ measurement fields of a substrate image to generate layer-thickness/ profile information and to use such information for further substrate processing. However, Iwanaga [as modified by Bartov and Dhandapani] does not expressly teach where Chen teaches:
in the step of selecting the plurality of measurement fields, excluding regions of the substrate from the selection that are located at least one of on an edge of the substrate or at a center of the substrate defined relative to the edge.
( [0044], [0047], [0049-0050]: Chen teaches defining “edge exclusion” for a wafer edge mask as part of the data-processing recipe, and further teaches that polar and Cartesian masks may be added to exclude certain areas of the wafer surface in the site metric calculation. Chen also teaches that wafer edge treatment is performed because edge exclusion can create data discontinuity and because strong wafer-edge response may affect detection in the wafer edge region. Therefore, Chen teaches selecting/ defining measurement sites while excluding edge regions and other masked wafer-surface regions from the site metric calculation, corresponds to excluding substrate regions located at least on an edge of the substrate from the selection. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Iwanaga [as modified by Bartov and Dhandapani] by incorporating Chen’s known wafer-edge exclusion and masking technique when selecting measurement fields. Iwanaga [as modified by Bartov and Dhandapani] performs image-based measurement and processing of substrate regions, while Chen teaches that wafer-edge regions can create strong filter responses and data discontinuities that affect wafer-site measurement results. A skilled artisan would have been motivated to exclude edge regions and other masked wafer-surface regions from measurement-field selection in order to reduce edge-related artifacts, avoid unreliable site measurements, and improve the accuracy of substrate-region profile information. The modification would have been the predictable use of a known wafer-metrology masking technique in a similar image-based wafer measurement process to obtain more reliable region-based measurements.
Regarding claim 6, Iwanaga [as modified by Bartov, Dhandapani and Chen] teaches the method according to claim 5, wherein the step of selecting the plurality of measurement fields comprises selecting measurement fields in regions of the substrate surface having the highest grayscale differences.
( [0061]: Chen teaches filtering, processing wafer surface images to enhance “surface feature" or "background contrast” and further teaches reporting or grouping site metric values using thresholds to identify site regions whose metric values are outside a variation range, significantly higher than their surroundings, which corresponds to selecting measurement fields having the highest grayscale differences relative to other regions. )
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Iwanaga [as modified by Bartov and Dhandapani], in view of Uziel (Uziel et al, US 2017/0084425 A1, 2017).
Regarding claim 7, Iwanaga [as modified by Bartov and Dhandapani] teaches the method according to claim 1,
Iwanaga [as modified by Bartov and Dhandapani] teaches image-based measurement of selected substrate regions/measurement fields for generating layer-thickness/ profile information. However, Iwanaga [as modified by Bartov and Dhandapani] does not expressly teach selecting measurement fields that partially overlap, where Uziel teaches:
wherein the step of selecting the plurality of measurement fields comprises selecting measurement fields that partially overlap.
( Uziel discloses that during scanning, the system acquires a plurality of image frames corresponding to multiple partially overlapping fields of view, which are used as the measurement fields, that can be consolidated to a single effective field of view [0062]. )
It would have been prima facie obvious to a POSITA, before the effective filing date of the claimed invention, motivated to modify Iwanaga’s step of selecting the plurality of measurement fields to select measurement fields that partially overlap as taught by Uziel, because using overlapping fields is a well-known imaging, metrology technique so that the resulting thickness-variation profile is more accurate and less sensitive to local noise or edge effects at the borders of non-overlapping fields. The modification is a predictable use of a known technique (overlapping fields) to improve measurement reliability in Iwanaga’s image-based thickness-profiling workflow.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
